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Background: DNA methylation (DNAm)-based measures of aging, termed epigenetic clocks (EC), are associated with aging-related outcomes including cardiovascular disease (CVD) and all-cause mortality. Associations of ECs with heart failure (HF) are unclear. We tested whether ECs were positively associated with risk of incident HF in the Atherosclerosis Risk in Communities (ARIC) study and evaluated whether adding ECs to Pooled Cohort Equation (PCE) variables improved risk prediction. Methods: We measured DNAm in peripheral blood leukocytes in 2,263 African American (mean age=56.3 years) and 925 European American (mean age=59.5 years) participants using the Illumina HM450K and calculated 7 ECs: Horvath, Hannum, extrinsic (EEAA) and intrinsic (IEAA) epigenetic age acceleration, Hannum, PhenoAge, and GrimAge. HF was ascertained by ICD- 9 code 428 and adjudication by an expert panel. We carried out race stratified proportional hazards regression to test associations ECs with incident HF, adjusting for PCE variables: age, sex, smoking, total cholesterol, HDL, systolic blood pressure (SBP), antihypertensive medication use, and diabetes. We calculated area under the curve (AUC) and integrated discrimination index (IDI) to evaluate improvement in risk prediction when adding the ECs to PCE variables. Results: The number of incident HF events and mean follow-up time in African Americans and European Americans were 640 (189 in the first 10 years) and 19.3 years, and 191 and 21.7 years, respectively. All 7 ECs were positively associated with HF in both African Americans and European Americans. In African Americans with follow-up restricted to the first 10 years, the HR for a one SD increment in GrimAge (5.64 years) was 1.57 (95% CI=1.31, 1.88), comparable to that for a one-SD (5.82 years) increment in age (HR=1.58, 95% CI=1.36, 1.83) and greater than that for a one-SD (20.2mmHg) increment in SBP (HR=1.33, 95% CI=1.18, 1.51). In European Americans across the entire follow-up period, the HR for a one-SD increment in GrimAge (6.13 years) was 1.22 (95% CI=1.06, 1.41), smaller than that for a one-SD (5.50 years) increment in age (HR=1.93, 95% CI=1.63, 2.29) and larger than that for a one-SD (17.9 mmHG) increment in SBP (HR=1.13, 95% CI=0.98, 1.30). In African Americans with follow-up restricted to the first 10 years, adding GrimAge to PCE variables increased AUC by 0.019 (95% CI=0.003, 0.035) and the IDI was 0.010 (95% CI=0.002, 0.019). In European Americans, adding GrimAge did not change AUC appreciably (0.004, 95% CI=-0.006, 0.014) and the IDI was 0.002 (95% CI=0.000, 0.005). Conclusion: ECs are positively associated with HF in African American and European American participants independent of traditional CVD risk factors. GrimAge modestly improved heart failure risk prediction in African Americans. HF-specific DNAm-based measures should be developed and evaluated for improvement in risk prediction.

Background: Sex hormones are associated with obesity, diabetes mellitus, and other stroke risk factors; however, studies on sex hormones and stroke risk report inconsistent results. We assessed the associations of testosterone and sex hormone binding globulin (SHBG) with risk of ischemic stroke among men and post-menopausal women in the Atherosclerosis Risk in Communities (ARIC) Study. Methods: A total of 4,349 men and 4,720 post-menopausal women who had SHBG and total testosterone measurements at visit 4 (1996-98) were followed through 2018 for the development of ischemic stroke. We examined log transformed SHBG and testosterone exposure as quartiles and as per 1 SD increment. We used Cox regression to estimate the hazard ratios (HR) for ischemic stroke, adjusting for demographic, behavioral and clinical variables. Analyses were stratified by sex and menopausal hormone therapy (HT) use. Results: Participants were aged 63±6 years at baseline, 52% were women (25% HT users), and 21% Black. There were 691 strokes over a median follow-up of 19.8 years. Mean log SHBG (nmol/L) and testosterone (nmol/L) were 4.3±0.7 and 3.1±0.5 for HT users, 3.6±0.7 and 3.2±0.5 for non-HT users, and 3.4±0.5 and 6.2±0.5 for men. Quartile 1 vs Q4 for SHBG and testosterone were ≤50.3 vs >121 and ≤16 vs >28 in HT users, ≤23.3 vs >55 and ≤17.9 vs >32.7 in non-HT users, and ≤23.3 vs >41.8 and ≤388 vs >657 in men. SHBG and testosterone were not significantly associated with stroke in any group (Figure). The HRs [95% confidence interval] for highest to lowest SHBG and testosterone quartiles were 1.04 [0.51-2.14] and 1.64 [0.85-3.17] in HT-users, 1.02 [0.70-1.48] and 1.16 [0.83-1.62] in HT non-users, and 0.96 [0.70-1.32] and 0.87 [0.63-1.21] in men, respectively. The HRs for stroke associated with 1 SD increase in SHBG and testosterone were 1.14 [0.88-1.46] and 1.19 [0.96-1.46] in HT users. Associations were also null among non-HT users and men. Conclusion: SHBG and testosterone were not associated with ischemic stroke risk in this cohort of older men and post-menopausal women.

Background Hip fractures are a significant post-stroke complication. We examined predictors of hip fracture risk after stroke using data from the Women's Health Initiative (WHI). In particular, we examined the association between post-stroke disability levels and hip fracture risk. Methods The WHI is a prospective study of 161,808 postmenopausal women aged 50-79 years. Trained physicians adjudicated stroke events and hip fractures. Our study included stroke survivors from the observational and clinical trial arms who had a Glasgow Outcome Scale of good recovery, moderately disabled, or severely disabled and survived more than 7 days post-stroke. Hip fracture-free status was compared across disability levels. Secondary analysis examined hip fracture risk while accounting for competing risk of death. Results Average age at time of stroke was 74.6±7.2 years; 84.3% were white. There were 124 hip fractures among 4,640 stroke survivors over a mean follow-up time of 3.1±1.8 years. Mortality rate was 23.3%. Severe disability at discharge (Hazard Ratio (HR): 2.1 (95% Confidence Interval (CI): 1.4-3.2), but not moderate disability (HR: 1.1 (95%CI: 0.7-1.7), was significantly associated with an increased risk of hip fracture compared to good recovery status. This association was attenuated after accounting for mortality. White race, increasing age and higher Fracture Risk Assessment Tool (FRAX)-predicted hip fracture risk (without bone density information) were associated with an increased hip fracture risk. After accounting for mortality, higher FRAX risk and white race remained significant. Conclusion Severe disability after stroke and a higher FRAX risk score were associated with risk of subsequent hip fracture. After accounting for mortality, only the FRAX risk score remained significant. The FRAX risk score appears to identify stroke survivors at high risk of fractures. Our results suggest that stroke units can consider the incorporation of osteoporosis screening into care pathways.

Background: There are substantial gaps in stroke morbidity in low and middle income countries (LMIC) compared to high income countries (HIC). Stroke incidence and mortality are higher in LMIC compared to HIC. While education has been associated with stroke incidence and common stroke outcomes in HIC, the pattern of this social determinant of health may be reversed in LMIC. We examined the association between stroke prevalence and depression, cognitive function and functional ability utilizing the WHO Study on Global AGEing and Adult Health (SAGE). We also assessed if education modified this association. Methods: We used data from the WHO SAGE, wave 1 (2007-10), which is a nationally representative cohort from six LMIC: China, Ghana, India, Mexico, Russia, and South Africa. Stroke prevalence was classified by self-reported stroke or stroke symptoms. Outcomes included 1) depression [ICD-10 diagnostic criteria]; 2) cognitive function [z-score of performance on executive function, verbal fluency, and memory]; and 3) functional ability [WHO Disability Assessment Schedule]. Generalized linear models were fit for the association between stroke prevalence and outcomes. For our secondary analysis, educational attainment was included as a modifier. All models were adjusted for demographics, socioeconomic status, and health variables. Results: We included 39,896 individuals who had data on their stroke status. Stroke prevalence was 5.1% (n=2,034). Stroke survivors had lower mean cognitive function (-0.1±.8 vs 0.1±0.7, p Conclusion: Stroke was associated with a higher odds of depression and higher risk of reduced cognitive function and functional ability. Unlike in HIC, these six LMIC did not display a significant inverse association between education and depression and cognitive function. Future studies should assess predictors of poorer post-stroke outcomes and if other socioeconomic measures modify consequences of stroke in LMIC.

PURPOSE: Hypertension (HTN) is significantly under-treated in stroke survivors. We examined usability and efficacy of a mHealth -based care model for improving post-stroke HTN control (Funding: AHRQ R21HS021794). METHODS: We used a RCT design. Planned study duration was 90 days. Intervention arm (IA) participants measured their BP daily using a smart phone and wireless BP monitor. This was transmitted automatically to the study database. Investigators (Physician + PharmD) made bi-weekly medication adjustments to achieve the BP goal. Control arm (CA) participants received a digital BP monitor and usual care. We examined Usability (measured with Marshfield System Usability Survey) and HTN control efficacy using an ITT (intent-to-treat) and as-treated (AT) analyses. RESULTS: Fifty participants (IA=28; CA = 22) completed the study. The Marshfield survey question, “I thought the system was easy to use” mean score was 4.6, (5 = strongly agree). Mean SBP declined significantly between enrollment and study completion in the IA. In ITT, IA SBP declined 9.88 mm, p=0.005. In AT, IA SBP declined 10.81 mm, p=0.0036. CA SBP decline was 5–6 mm Hg (not significant). In the ITT, baseline HTN control (SBP < 140 mm Hg) was 50% in IA and CA. At study completion, HTN was controlled in 82% (23/28) of IA and 64% (14/22) of CA (p = 0.14). In the AT, HTN was controlled in 89% (23/26) of IA and 58% (14/24) of CA, (p=0.015). CONCLUSION: A mHealth-based HTN care model had excellent usability and provided better HTN control than usual care in stroke survivors.

Background: Hip fractures are a significant post-stroke complication. While studies have shown an increased incidence of hip fractures post-stroke, the relationship between stroke-related disability and hip fracture rates are not well characterized. Herein, we examine factors associated with hip fracture risk after stroke using the Women’s Health Initiative (WHI) data. Methods: The WHI is a prospective study of 161,808 postmenopausal women aged 50-79 years. In the WHI, stroke cases were initially self-reported and then confirmed by a neurologist adjudication using medical records and a manual. Hip fracture cases were self-reported and then radiologically confirmed by physician adjudication. We included stroke survivors from the observational and clinical trial arms who had a Glasgow Outcome Score (GOS) of 1-3 (good recovery, moderately disabled, severely disabled) and survived at least one week after stroke (n=4,640). Survival free from any radiologically-confirmed hip fracture post-stroke was estimated and compared by GOS status. Secondary analysis examined the post-stroke hip fracture risk while accounting for the competing risk of death. Results: There were 124 hip fractures. Average age of stroke was 74.6 ± 7.2 years. Mean follow-up time was 3.1±1.8 years. Hip fractures by GOS status represented 2.4% (45/1872), 2.5% (34/1366), and 3.5% (45/1278) among good recovery, moderately disabled, and severely disabled. In the competing risk model, 23.3% (1079/4640) of the participants died before the end of follow-up or the occurrence of a hip fracture. Severely disabled (HR: 2.1 (95% CI: 1.3, 3.2), p=0.001) status, but not moderately disabled (HR: 1.1 (95% CI: 0.7, 1.7) p=0.8), was significantly associated with an increase in risk of hip fracture compared to good recovery status. This association was attenuated and not significant for moderately disabled (HR: 1.1 (95%CI: 0.7, 1.7), p=0.8) and severely disabled (HR 1.5 (95%CI: 1.0, 2.3), p=0.06) status when accounting for mortality after stroke. High Hip Fracture Risk Assessment Tool (FRAX) risk (without bone density information), being Caucasian, and increasing age were associated with an increased post stroke hip fracture risk. When accounting for mortality after stroke, higher FRAX-predicted hip fracture risk and race/ethnicity remained significant. The association between age and hip fracture was attenuated and not significant after accounting for post-stroke mortality. Conclusion: Among stroke survivors, severely disabled status and FRAX-predicted hip fracture risk were associated with a higher risk of subsequent radiologically confirmed hip fracture, but only the FRAX-predicted hip fracture risk association remained significant when considering the competing risk of mortality after stroke. Interventions to reduce fracture risk after stroke should be evaluated in clinical trials.